{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-generalized-method-of-moments","title":"Adversarial Generalized Method of Moments","arxiv_id":"1803.07164","date":"2018-03-19","proceeding":null,"authors":["Greg Lewis","Vasilis Syrgkanis"],"abstract":"We provide an approach for learning deep neural net representations of models\ndescribed via conditional moment restrictions. Conditional moment restrictions\nare widely used, as they are the language by which social scientists describe\nthe assumptions they make to enable causal inference. We formulate the problem\nof estimating the underling model as a zero-sum game between a modeler and an\nadversary and apply adversarial training. Our approach is similar in nature to\nGenerative Adversarial Networks (GAN), though here the modeler is learning a\nrepresentation of a function that satisfies a continuum of moment conditions\nand the adversary is identifying violating moments. We outline ways of\nconstructing effective adversaries in practice, including kernels centered by\nk-means clustering, and random forests. We examine the practical performance of\nour approach in the setting of non-parametric instrumental variable regression.","url_abs":"http://arxiv.org/abs/1803.07164v2","url_pdf":"http://arxiv.org/pdf/1803.07164v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adversarial-generalized-method-of-moments","repo_url":"https://github.com/vsyrgkanis/adversarial_gmm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.07164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.07164"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/vsyrgkanis/adversarial_gmm","reach":null}],"summary":{"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"b0032907933ab1fd","entry":"get_data","repo":"vsyrgkanis/adversarial_gmm","repo_kind":"official","path":"monte_carlo.py","file_url":"https://github.com/vsyrgkanis/adversarial_gmm/blob/HEAD/monte_carlo.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b0032907933ab1fd"}},{"code_sha256_prefix":"86139baea5060e26","entry":"deep_iv_fit","repo":"vsyrgkanis/adversarial_gmm","repo_kind":"official","path":"monte_carlo.py","file_url":"https://github.com/vsyrgkanis/adversarial_gmm/blob/HEAD/monte_carlo.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"86139baea5060e26"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}